The impact of schools on young people's transition to university

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Sector is again a critical factor, and the consistent role <strong>of</strong> school-level socioeconomic status across all government <strong>schools</strong> deserves attention. 23 Government <strong>schools</strong> with average socioeconomic status and without students whose language background is other than English (node 13) show an 8% higher predicted probability <strong>of</strong> university enrolment than <strong>schools</strong> in the same category whose socioeconomic status is one standard deviation below the average (node 11). When comparing the latter group in node 11 with the group <strong>of</strong> non-government <strong>schools</strong> with a high proportion <strong>of</strong> students with a language background other than English (node 10), the difference in the predicted probability <strong>of</strong> university attendance is 37.3 percentage points. Again, however, blanket statements about a ‘general’ positive effect from higher proportions <strong>of</strong> these students need to be qualified, given the potentially complex interactions with other background factors, such as refugee status. Further exploration <strong>of</strong> influential school attributes This part <strong>of</strong> the analysis explores the distribution <strong>of</strong> school performance in more detail. <strong>The</strong> caterpillar plots in figures 6 and 7 rank each school by its predicted performance, where the predictions are based on the influential school attributes identified through multi-level modelling. To better assess the net school effect on each <strong>of</strong> the two outcomes, individual background variables are held constant at their mean values (that is, all students in the sample are ‘made’ to have the same average background characteristics). A distinctive feature <strong>of</strong> the Australian school system is the significant proportion <strong>of</strong> students in the nongovernment sectors. To show the variation in school performance within and across sectors, caterpillar plots for each sector are provided below. Caterpillar plots can be interpreted as follows. For the TER outcome, the first caterpillar plot in figure 6 (‘all <strong>schools</strong>’) shows how far the average TER <strong>of</strong> each individual school deviates from the average TER across all <strong>schools</strong> in the sample (that is, the grand mean). <strong>The</strong> grand mean TER <strong>of</strong> 76 points is represented by the horizontal zero line across the caterpillar plots. Each school in the plot features 95% confidence intervals, which capture the range <strong>of</strong> prediction error. <strong>The</strong> size <strong>of</strong> prediction error decreases as the number <strong>of</strong> students per school in the sample increases and vice versa. A school whose confidence interval does not cross the horizontal zero line has an average TER that is significantly different from the grand mean. <strong>The</strong> interpretation <strong>of</strong> the caterpillar plots for university enrolment in figure 7 follows the same logic, although here the horizontal zero line shows the grand mean probability <strong>of</strong> university enrolment <strong>of</strong> 51.3%. <strong>The</strong> left and right tail ends <strong>of</strong> the caterpillar plot for ‘all <strong>schools</strong>’ in figure 6 show that a considerable number <strong>of</strong> <strong>schools</strong> perform above and below average with respect to TER. When examining the different sectors, government <strong>schools</strong> are more densely concentrated at the lower end <strong>of</strong> the TER scale, whereas Catholic and independent <strong>schools</strong> are better represented at the higher end. <strong>The</strong> picture is similar with respect to university enrolment (figure 7), with the performance advantage <strong>of</strong> non-government <strong>schools</strong> becoming even more pronounced. However, several Catholic and independent <strong>schools</strong> do perform significantly below average. 23 Readers are reminded that the school-level socioeconomic status measure is standardised to a mean <strong>of</strong> 1 and a standard deviation <strong>of</strong> 0. 26 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
Figure 6 Caterpillar plots <strong>of</strong> between-school differences for TER after multi-level modelling Note: <strong>The</strong> background characteristics <strong>of</strong> individuals are held constant at their mean values. All individual plots for government, Catholic, and independent <strong>schools</strong> reference the grand mean across all <strong>schools</strong> in the sample. NCVER 27
Page 1: LONGITUDINAL SURVEYS OF AUSTRALIAN
Page 4 and 5: © Commonwealth of
Page 7 and 8: Contents Tables and figures 6 Execu
Page 9 and 10: 8 Cluster analysis of</stro
Page 11 and 12: can be identified statistically but
Page 13 and 14: Current knowledge about school effe
Page 15 and 16: Analysis Data and sample This study
Page 17 and 18: Academic orientation Academic orien
Page 19 and 20: Results This section first presents
Page 21 and 22: School attribute Categories ß SE J
Page 23 and 24: variance which can be attributed to
Page 25 and 26: School attribute Categories ß SE O
Page 27: student—teacher ratio, competitiv
Page 31 and 32: Comparing influential school attrib
Page 33 and 34: in over two-thirds of</stro
Page 35 and 36: Table 8 Geographic distribution <st
Page 37 and 38: Figure 10 Components of</st
Page 39 and 40: References Australian Curriculum, A
Page 41 and 42: Appendix A: Descriptive statistics
Page 43 and 44: School attribute Categories TER (N
Page 45 and 46: Perceptions of the
Page 47 and 48: Figure C1 Scree plot from SES facto
Page 49 and 50: Figure D1 Scree plot from perceptio
Page 51 and 52: In this paper, the ICC for the cont
Page 53 and 54: Table F2 Results for student-level
Page 55 and 56: School attribute Categories df Join
Page 57 and 58: Table F5 Results for school-level p
Page 59: Appendix G: Information on the Logi

The impact of schools on young people's transition to university

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